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Person re-identification has received considerable attention in the image processing, computer vision and pattern recognition communities because of its huge potential for video-based surveillance applications and the challenges it presents due to illumination, pose and viewpoint changes among non-overlapping cameras. Being different from the widely used low-level descriptors, visual attributes (e...
Traffic surveillance has always been a challenging task to automate. The main difficulties arise from the high variation of the vehicles appertaining to the same category, low resolution, changes in illumination and occlusions. Due to the lack of large labeled datasets, deep learning techniques still have not shown their full potential. In this paper, we train an Ensemble of Deep Networks (EDeN) to...
The focus of this paper is on presentation attack detection for the iris biometrics, which measures the pattern within the colored concentric circle of the subjects' eyes, to authenticate an individual to a generic user verification system. Unlike previous deep learning methods that use single convolutional neural network architectures, this paper develops a framework built upon triplet convolutional...
Person reidentification consists of recognizing individuals across different sensors of a camera network. Whereas clothing appearance cues are widely used, other modalities could be exploited as additional information sources, like anthropometric measures and gait. In this paper, we investigate whether the reidentification accuracy of clothing appearance descriptors can be improved by fusing them...
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